Nonlinear dynamic matrix control with data reconciliation algorithm to a polymerization system

The autocatalytic nature and multivariability of polymeric processes make the mathematical model process highly nonlinear. In addition, it is necessary to consider that the measured variable is subject to different degrees to error and so must be reconciliated aiming So avoid degradation of the process and system control performances. This work presents a procedure to establish a dynamic data reconciliation into a feedback control loop obtained by minimization of special designed objective function. The proposed methodology is based on the use of nonlinear predictive control techniques - Nonlinear Dynamic Matrix Control (NDMC) - associated to data reconciliation procedures, which is based on Nonlinear Programming (NLP) techniques. The algorithm developed will be implemented computationaly to a styrene polymerization system. The predictive control techniques are robust and when applied to polymeric system allows to make the system to have desirable dynamic characteristics.